Abstract 3820: Discovery of adaptive resistance pathways and anti-resistance combination therapies from phosphoproteomic data using graphical models
Authors: Augustin Luna, Heping Wang, Ozgun Babur, Chris Sander, Anil Korkut
Journal: Cancer Research (2019)

Abstract Introduction. Resistance to targeted therapies, either intrinsic (pre-existing at the time of treatment) or acquired (emerges as the tumor adapts to therapy), is a major challenge in oncology. Blocking multiple escape routes using drug combinations is the best solution to drug resistance. However, the discovery of effective combinations remains a challenging task due to complexity of the underlying biological processes and inter-tumor heterogeneity. Here, we developed a statistical pathway analysis method that (i) reveals pathways involved in drug activity and adaptive resistance and (ii) nominates combination therapies to down-regulate the resistance pathways. The method is based on the rationale that (i) targeting a specific genomic aberration may lead to activation of compensatory pathways (e.g. via feedback loops in the short term) and subsequent resistance, and (ii) collective changes in pathway activities are better predictors of resistance and mitigation strategies than abundances of individual molecules. Method. We construct a network model of signaling interactions using an adjusted graphical LASSO (GLASSO) algorithm from RPPA data. This is supplemented with prior pathway information from multiple signaling databases (using Pathway Commons) to estimate sparse directed graphical models. Next, we combine the network with the cell- type-specific drug response data to calculate a target score (TS) for each protein. The TS quantifies the adaptive pathway responses to a perturbation by integrating the change in the level of a (phospho)protein and its pathway neighborhood in response to a perturbation. A high TS corresponds to involvement in adaptive response (e.g. RTK upregulation by MEK inhibitor via a feedback loop) and a low TS corresponds to the activity of the drug (e.g. ERK phosphorylation inhibition by MEK inhibitor). Finally, by identifying the subnetworks with enriched, high TS values, we determine the adaptive resistance pathways. We validate the resulting predictions (combinations of the original drug perturbation with drugs targeting the resistance pathways) experimentally. Applications. The method is amenable to calculations for hundreds of samples treated with individual (or combinations of) drugs in multiple doses and/or time points and interrogated for thousands of molecular entities (mRNA or proteomic). With longitudinal data, we will be able to explain the evolution of drug resistance and potentially the optimum time points for intervention. The protocol is defined with a focus on RPPA data, but can be adapted to other kinds of molecular data associated with adaptive responses. We applied our method to BET-BRD inhibition in ovarian and breast cancers to compare resistance/response pathways in cells with varying sensitivity. The analysis nominated cell-type-specific anti-resistance combinations involving BET inhibitors. Citation Format: Augustin Luna, Heping Wang, Ozgun Babur, Chris Sander, Anil Korkut. Discovery of adaptive resistance pathways and anti-resistance combination therapies from phosphoproteomic data using graphical models [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 3820.